Massimo Fornasier — accepted AI-conference papers

2026

Consensus-based optimization (CBO): Towards Global Optimality in Robotics

Xudong Sun, Armand Jordana, Massimo Fornasier, Jalal Etesami, Majid Khadiv

RSS 2026poster

Zero-order optimization has recently received significant attention for designing optimal trajectories and policies for robotic systems. However, most existing methods (e.g., MPPI, CEM, and CMA-ES) are local in nature, as they rely on gradient estimation. In this paper, we introduce consensus-based …

Cited by 2 Source ↗Scholar ↗

2026

Fast training of accurate physics-informed neural networks without gradient descent

Chinmay Datar, Taniya Kapoor, Abhishek Chandra, Qing Sun, Erik Lien Bolager, Iryna Burak +3

ICLR 2026oral

Solving time-dependent Partial Differential Equations (PDEs) is one of the most critical problems in computational science. While Physics-Informed Neural Networks (PINNs) offer a promising framework for approximating PDE solutions, their accuracy and training speed are limited by two core barriers:…

Cited by 0 Source ↗Scholar ↗